Analysing the Surface Morphology of Colorectal Polyps:Differential Geometry and Pit Pattern Prediction

We present an initial study analysing the surface morphology of colorectal polyps from optical projection tomography. The differential geometry of polyp surfaces, seg-mented using a level sets method, is explored in terms of local, multi-scale shape index and curvedness descriptors. A surface region of interest can be represented using his-tograms of these descriptors. An experiment is described investigating the ability to predict pit pattern categories from these histograms using support vector machines.

Local structure prediction for gland segmentation

We present a method to segment individual glands from colon histopathology images. Segmentation based on sliding window classification does not usually make explicit use of information about the spatial configurations of class labels. To improve on this we propose to segment glands using a structure learning approach in which the local label configurations (structures) are considered when training a support vector machine classifier. The proposed method not only distinguishes foreground from background, it also distinguishes between different local structures in pixel labelling, e.g. locations between adjacent glands and locations far from glands. It directly predicts these label configurations at test time. Experiments demonstrate that it produces better segmentations than when the local label structure is not used to train the classifier

Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks

We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. For HC-SVM we further observe that training a second-level window classifier on the posterior probabilities - as an output refinement - can substantially improve the segmentation performance. The final performance of HC-SVM with refinement is comparable to that of CNN. Furthermore, we show that by combining and refining the posterior probability outputs of CNN and HC-SVM together, a further performance boost is obtained

An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens

AbstractImmunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test data sets were 87.1% and 88.5% for cell and specimen classification respectively. These were the highest achieved in the competition, suggesting that our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods

Ethnic differences in out-of-hospital cardiac arrest among Middle Eastern Arabs and North African populations living in Qatar

Aims: There are very few studies comparing epidemiology and outcomes of out-of-hospital cardiac arrest (OHCA) in different ethnic groups. Previous ethnicity studies have mostly determined OHCA differences between African American and Caucasian populations. The aim of this study was to compare epidemiology, clinical presentation, and outcomes of OHCA between the local Middle Eastern Gulf Cooperation Council (GCC) Arab and the migrant North African populations living in Qatar. Methods: This was a retrospective cohort study of Middle Eastern GCC Arabs and migrant North African patients with presumed cardiac origin OHCA resuscitated by Emergency Medical Services (EMS) in Qatar, between June 2012 and May 2015. Results: There were 285 Middle Eastern GCC Arabs and 112 North African OHCA patients enrolled during the study period. Compared with the local GCC Arabs, univariate analysis showed that the migrant North African OHCA patients were younger and had higher odds of initial shockable rhythm, pre-hospital interventions (defibrillation and amioderone), pre-hospital scene time, and decreased odds of risk factors (hypertension, respiratory disease, and diabetes) and pre-hospital response time. The survival to hospital discharge had greater odds for North African OHCA patients which did not persist after adjustment. Multivariable logistic regression showed that North Africans were associated with lower odds of diabetes (OR 0.48, 95% CI 0.25–0.91, p = 0.03), and higher odds of initial shockable rhythm (OR 2.86, 95% CI 1.30–6.33, p = 0.01) and greater scene time (OR 1.02 95% CI 1.0–1.04, p = 0.02). Conclusions: North African migrant OHCA patients were younger, had decreased risk factors and favourable OHCA rhythm and received greater ACLS interventions with shorter pre-hospital response times and longer scene times leading to better survival.Peer reviewedFinal Accepted Versio

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

BACKGROUND: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review. METHODS: In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software. RESULTS: Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; [Image: see text]=0.91, Quickscore; [Image: see text]=0.92). CONCLUSIONS: The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials

Exploring adaptive Expertise as a target for engineering design education

ABSTRACT In this paper we present the concept of adaptive expertise and relate this concept to the design curriculum offered by the Institute for Design Engineering and Applications (IDEA) at Northwestern University. The model of adaptive expertise suggests that instruction and assessment include a balance of "efficiency" and "innovation". These two dimensions are first described from a theoretical perspective, then are discussed in more concrete terms in the context of the design experiences provided in IDEA. The model of adaptive expertise suggests that by providing learning experiences that balance these two dimensions we better prepare students to flexibly apply their knowledge in innovative ways. Since these aims are so closely aligned with the goals of design, we offer adaptive expertise as the target for engineering design education

Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a)

Plasma lipoproteins are important carriers of cholesterol and have been linked strongly to cardiovascular disease (CVD). Our study aimed to achieve fine-grained measurements of lipoprotein subpopulations such as low-density lipoprotein (LDL), lipoprotein(a) (Lp(a), or remnant lipoproteins (RLP) using electron microscopy combined with machine learning tools from microliter samples of human plasma. In the reported method, lipoproteins were absorbed onto electron microscopy (EM) support films from diluted plasma and embedded in thin films of methyl cellulose (MC) containing mixed metal stains, providing intense edge contrast. The results show that LPs have a continuous frequency distribution of sizes, extending from LDL (> 15 nm) to intermediate density lipoprotein (IDL) and very low-density lipoproteins (VLDL). Furthermore, mixed metal staining produces striking “positive” contrast of specific antibodies attached to lipoproteins providing quantitative data on apolipoprotein(a)-positive Lp(a) or apolipoprotein B (ApoB)-positive particles. To enable automatic particle characterization, we also demonstrated efficient segmentation of lipoprotein particles using deep learning software characterized by a Mask Region-based Convolutional Neural Networks (R-CNN) architecture with transfer learning. In future, EM and machine learning could be combined with microarray deposition and automated imaging for higher throughput quantitation of lipoproteins associated with CVD risk.Publisher PDFPeer reviewe